91

AN ANALYSIS OF DEER-VEHICLE COLLISIONS: THE 

CASE OF OHIO

KURT A. SCHWABE, PETER W. SCHUHMANN, MICHAEL J. TONKOVICH AND ELLEN WU

Abstract: The costs of deer-vehicle collisions (DVCs) in Ohio are estimated to be in excess of US$52 million annually. The 

intention of this paper is to identify factors contributing to the abundance of DVCs in Ohio, calculate the average cost of a deervehicle collision event, and illustrate the potential gains in economic efficiency from alternative approaches for reducing DVCs. 

Our results suggest that large potential economic gains from reducing DVCs in Ohio exist and that the optimal strategies for 

achieving these reductions seem to combine both changes in deer management schemes and deer-vehicle mitigation strategies.

Key words: bioeconomics, deer-vehicle collisions, Ohio, panel data, wildlife valuation

In 1994, Ohio had nearly 26,000 reported deervehicle collisions (DVCs), increasing by nearly 60%, 

or at an annual rate of approximately 1,600 cases, 

since 1989. These trends are not surprising given the 

increases in both white-tailed deer (Odocoileus virginianus – deer, hereafter) populations and traffic volume. 

From 1989 to 1994, buck-gun harvest per square mile, 

which is defined as the number of bucks harvested 

during the 2-week shotgun season and has been shown 

to be highly correlated with deer population (Culbertson and Stoll 1990), increased by nearly 62%. Over this 

same 5-year period, the number of registered vehicles, 

which serves as a proxy for traffic volume, increased by 

roughly 20%. These trends, moreover, are not unique to 

Ohio. Cook and Daggett (1995) find that incidences of 

DVCs in a number of midwestern states have increased 

significantly over a recent 10-year period. Indeed, of 

Ohio’s 5 contiguous states and Wisconsin, the average 

annual number of reported DVCs per state from 1989 

to 1994 was approximately 23,000, with Kentucky and 

Michigan having the lowest and highest averages (4000 

and 49,000, respectively) and Ohio falling in the middle 

with nearly 21,400 (Tonkovich 1995). Nationally, over 

538,000 DVCs were reported in 1991 (Romin and Bissonette 1996a).

One need only acknowledge the estimated costs 

associated with these DVCs to understand why deer 

management goals, such as those purported by the Ohio 

Department of Natural Resources Division of Wildlife 

(ODNR), include managing deer populations with an 

eye on conflicts with motor travel. For instance, the 

Ohio Department of Public Safety (ODPS 1997), using 

the National Highway Traffic Safety Administration’s 

average cost per accident by severity of accident, estimated that the costs associated with DVCs in 1996 

exceeded US$52 million (in 1996 dollars, as all dollar 

estimates in the following will be indexed). Work 

by Decker et al. (1990) suggested that the reported 

57,000 DVCs in New York in 1988 resulted in an estimated US$66.3 million in vehicle damage. Nationwide, 

Conover et al. (1995) estimated that DVCs cost approximately US$1.2 billion annually.

Yet while large deer populations may lead to 

more DVCs and other types of negative deer-human conflicts (e.g., crop damage), these populations also generate social benefits. That is, there are both consumptive and nonconsumptive values associated with deer. 

For instance, Loomis et al. (1989) estimated that total 

consumer surplus from deer hunting in California in 

1987 was US$230 million whereas the consumer surplus 

from viewing deer was approximately US$43 million. 

In Ohio, hunters spent nearly US$15 million in 1997 on 

permits and licenses for the opportunity to hunt deer. 

Furthermore, Schwabe et al. (2001) estimated that Ohio 

hunters would have been willing to pay nearly US$1.4 

million in 1996 for an additional day of deer hunting. 

Indeed, these numbers highlight another component 

of many states deer management goals – to maximize 

the recreational benefits associated with hunting, viewing, and photographing deer. From an economic perspective, then, deer management goals are essentially 

intended to achieve and maintain that population level 

where the differences between the benefits and costs – 

i.e., net benefits – are greatest.

The objectives of this research are to identify 

factors contributing to the abundance of deer-vehicle 

collisions in Ohio, calculate the average cost of a deervehicle collision event, and illustrate the potential gains 

in economic efficiency from alternative approaches for 

DVCs. Using data at both the county-level and the individual road-segment level from Ohio, we attempt to 

illustrate the extent to which deer-vehicle collisions are 

affected by deer population size, traffic volume, location, harvest quantities, and mitigation efforts. Using 

the regression results from 1 set of analyses, we then 

illustrate the potential biological and economic implications of 2 general strategies for reducing DVCs. While 

the focus of our exercise is on Ohio and a few selected 

counties within Ohio that differ with respect to deer 

populations, deer habitat, and number of vehicles, our 
92

HUMAN CONFLICTS WITH WILDLIFE: ECONOMIC CONSIDERATIONS

general approach can be applied to any management 

unit for controlling deer populations.

RELATED LITERATURE

There are essentially 3 strands of literature associated with DVCs: the direct costs of DVCs, mitigation 

efforts for reducing DVCs, and the potential relationships between DVCs and deer population size. An 

equally important strand of literature is the environmental and natural resource economics literature related to 

optimal deer management. This latter strand, notably 

works by Keith and Lyon (1985) and Cooper (1993), 

employs a bioeconomic framework and evaluates optimal management strategies with consideration to biological and economic factors.

Direct Costs of DVCs 

The direct costs associated with DVCs can be 

categorized into 2 main areas: vehicle accident costs 

and deer losses. Results in the literature associated with 

vehicle accident costs have varied quite dramatically. In 

a summary of vehicle-accident costs by Conover et al. 

(1995), the average vehicle repair bill varied between 

US$1,303 in Michigan (Hansen 1983) to nearly US$2,389 

in Pennsylvania (Witmer and deCalesta 1992). Romin 

and Bissonette (1996a), in one of the most comprehensive studies of property damage from DVCs, used 

unpublished data from the Vermont Department of 

Transportation and estimated that the average cost per 

accident, given 10 years of data covering 24,884 DVCs, 

was US$2,103. A large part of the variation in the vehicle accident costs is likely due to differences in both 

methodology and definition. For instance, Reed et al. 

(1982) surveyed vehicle repair costs from Colorado State 

Patrol accident reports and benchmarked those against 

accident values as reported in claims to insurance companies. Alternatively, Hansen (1983) surveyed drivers 

who had submitted accident reports to the Michigan 

State Police to obtain cost estimates. 

DVC literature incorporating the costs associated 

with human injury and fatalities are limited. This is due 

to both the difficulties associated with assigning costs 

to these events (Reed et. al. 1982), and the fact that 

the percentages of collisions resulting in human injury 

or fatality are quite low, approximately 4% (Stoll et al. 

1985) and 0.029% (Rue 1989), respectively. Two studies 

that do acknowledge these components include Hansen 

(1983) and Romin and Bissonette (1996a). Hansen 

(1983) reports US$173 as the average cost associated 

with the morbidity and mortality effects of a DVC, 

including medical costs, lost wages, and the value of a 

statistical life. Romin and Bissonette (1996a) reported 

that approximately 120 people are killed annually due 

to DVCs. Using a value of life statistic of US$1.5 million 

that they cited from the Federal Highway Administration, the yearly cost associated with DVC-related human 

mortality is nearly US$180 million. 

In addition to damages to vehicles and humans, 

DVCs often result in deer fatality. Of the collisions surveyed by Allen and McCullough (1976), deer fatalities 

occurred in approximately 92% of DVCs. Within the 

DVC-related literature, the value of a deer ranges from 

US$965 (Reed et al. 1982) to US$1,468 (Romin and Bissonette 1996a). Within the environmental and natural 

resource economics literature, values have been estimated at US$35 (Livengood 1983), US$64 (Keith and 

Lyon 1985), US$209 (Loomis et al. 1989), and US$182 

(Schwabe et al. 2001). While these values all pertain 

to hunting values, they are estimated with different 

nonmarket valuation techniques, in different regions of 

the U.S., and for different species of deer.

For instance, Livengood (1983) uses a hedonic 

model and estimates the value of an additional whitetail 

deer for hunting in Texas. Keith and Lyon (1985) use a 

household production function approach and estimate 

the value of an additional mule deer for hunting in 

Utah. Loomis et al. (1993) used the contingent valuation 

method and estimated the value of a mule buck for 

hunting in California. Finally, Schwabe et al. (2001) used 

a random utility model to estimate the value of an additional whitetail deer for hunting in Ohio.

DVC Mitigation Strategies

There are numerous proposed, tried, and evaluated mitigation strategies to decrease DVCs, ranging 

from methods that aim to reduce deer appearance on 

highways to strategies that seek to increase human perception or the awareness of the human presence associated with DVCs (Primo and Primo 1996). With reference 

to the first category, strategies include, but are not limited to, different types of fencing (Halls et al. 1965, Falk 

et al. 1978, Feldhamer et al. 1986), underpass structures 

and overpass structures (Reed et al. 1975), reflectors 

(Reeves and Anderson 1993, Pafko and Kovach 1996, 

Ujvari et al. 1998), and intercept feeding (Wood and 

Wolfe 1988). Deer warning signs (Pojar et al. 1975) 

and deer whistles (Romin and Dalton 1992) are strategies that attempt to increase human perception or the 

awareness of human presence. Both Tonkovich (1995) 

and Romin and Bissonette (1996a) provide a detailed 

summary of these strategies. 

The lack of adequate control of extraneous variables and poor study design in field experiments has 

compromised a great deal of the DVC research. For 

example, Tonkovich (1995) noted that many DVC studies involving reflectors, and in particular Swareflex 

reflectors, may have been plagued by either or both 

of these factors. Because annual fluctuations in deer 
93

numbers occur and are the result of a multitude of 

factors, it is essential that multiple years of data with 

adequate control for untreated sites be used. In this way, 

it should be possible to reduce the likelihood that the 

observed research findings are due to factors other than 

the treatment. Such factors, including population size, 

weather, habitat conditions, are likely to impact deer 

movement and DVCs and thus should be monitored.

DVCs and Deer Populations

Whether highway losses from DVCs are strictly 

additive, partially compensatory, or strictly compensatory may have important implications on long terms 

trends in deer population (Lehnert 1996). This, in 

turn, may have implications on harvest rates. Recent 

papers that shed some light on the relationship between 

DVCs and deer population are Sitar et al. (1998), and 

Lehnert et al. (1996). In their analysis of white-tailed 

deer in Michigan, Sitar et al. (1998) found that roadkill 

accounted for 11.1% and 7.1% of the nonmigratory 

and migratory deer mortality, respectively. Alternatively, 

research by Lehnert et al. (1996) of mule deer in Utah 

found that highway mortality removes between 5.6% 

and 17.4% of the population each year. Lehnert et 

al. then compared 3 simulation models that assumed 

highway losses are strictly additive, partially compensatory, or strictly compensatory to see which model best 

explained the observed behavior. Their results indicated 

that highway losses were partially compensatory, suggesting that deer mortality from DVCs was not completely offset by mortality arising from other factors 

(e.g., hunting, predation, starvation, etc.).

Optimal Deer Management 

Following the seminal work by Brown and Hammack (1973) on wildlife-related recreational management, the use of bioeconomic modeling for optimal 

wildlife management exercises has been most prominent in the evaluations of alternative fishery management policies (McConnell and Sutinen 1979, Bockstael 

and McConnell 1981, Wilson 1982, Schuhmann and 

Easley 2000). Yet 2 studies that use bioeconomic models 

to evaluate optimal deer management include Keith 

and Lyon (1985) and Cooper (1993). As mentioned in 

Schuhmann and Schwabe (this issue), Keith and Lyon 

(1985) derived parameters that defined the relationships 

between a mule deer herd’s population dynamics, 

hunter utility, and the marginal value of an additional 

deer. Using data on mule deer populations from Robinette et al. (1977) and assuming hunter harvest is proportional to herd size, they estimated that the value of 

an additional deer is US$64. Their model, though, did 

not distinguish between bucks, does, and fawns and 

they did not include the costs of maintaining the deer 

herd – an omission they noted is required to achieve the 

economically efficient herd size.

Alternatively, Cooper (1993) formulated a bioeconomic model for estimating the optimal level of 

deer and tag sales. For the biological component of 

his model, he used the Killvary population simulation 

model developed at the California Department of Fish 

and Game (Updike 1990). For the value estimates of 

a deer, estimates from a contingent valuation survey 

on California deer by Loomis et al. (1989) were used. 

Both consumptive and nonconsumptive values were 

considered. Estimates of the consumptive and nonconsumptive value of an average buck were approximately 

US$209 and US$20, respectively.

CHARACTERISTICS OF DVCS IN OHIO

Methods

Assuming that the current number of DVCs in 

a given area is greater than optimal, economic efficiency suggests that DVCs should be reduced to the 

point where the additional benefits gained from reducing DVCs are equal to the additional costs incurred 

at the margin. The benefits of reducing DVCs, as identified above, include reductions in property damage, 

human morbidity and mortality, and the loss of deer 

from DVCs. The costs, alternatively, will likely depend 

on the method or methods employed to reduce DVCs. 

Two general methods include installing various mitigation strategies such as fencing, signs, or reflectors, and 

reducing the deer population. Regardless of method, 

attaining the optimal number of DVCs will require that 

relationships be established between DVCs, the factors 

giving rise to them, and the resulting impacts on benefits and costs. 

To help formalize our discussion of these potential relationships, we hypothesize, based on much of the 

research described above, that DVCs are influenced by 

the following factors:

DVCC = f (deer population densityC, traffic densityC, 

mitigation strategiesC, proximity to urban areaC,

habitatC, time of year, time of day,

day of week, road conditionsC, 

weather conditionsC) (1)

where c identifies the location of the DVC. While these 

factors are not all encompassing, for instance vehicle 

speed is not included, they do include many of the 

major factors influencing DVCs in a particular area. Consider DVC characteristics in Ohio. Both traffic volume 

and DVCs have been on a steady rise. As shown 

in Tonkovich (1995), between 1977 and 1994, traffic 

volume on Ohio’s highways rose at an average annual 

rate of 3.2% while DVCs increased in rural and urban 

areas on average 7% annually. Tonkovich also showed 

that, while day of the week seemed to have no influence 

on incidences of DVCs, month of the year and time of 

day were strongly correlated with DVCs. The 3 months 

DEER-VEHICLE COLLISIONS
94

HUMAN CONFLICTS WITH WILDLIFE: ECONOMIC CONSIDERATIONS

with the highest incidences of DVCs was November, 

accounting for approximately 26% of the yearly total, 

and October and December, each with approximately 

15% of the yearly total. DVCs across the remaining 

months ranged between approximately 7.5% of the 

yearly total in January down to roughly 3% in August. 

The highest occurrences of DVCs happened after 1700 

hr (approximately 58%) and between 0500 hr and 0700 

hr (approximately 20%). Clearly, the rut was a large 

component of these temporal effects. Finally, Tonkovich 

(1995) found that the majority of DVCs occurred under 

no adverse weather or road conditions. 

Based on these 2-way relationships, most of the 

factors mentioned above seem to have either a strong 

positive or strong negative relationship with DVCs. 

Despite this evidence, a more rigorous analysis that 

accounts for other potentially confounding factors is 

required to effectively capture the relationship between 

any pair of variables. Indeed, both Tonkovich (1995) 

and Romin and Bissonette (1996a) called for more rigorous and sound research (rather than opinion) when 

drawing conclusions about the relationships between 

DVCs and various mitigation strategies. At the risk of 

exposing ourselves to similar criticism, we attempt to 

provide such an analysis in the remaining part of this 

section.

Results 

The first relationship we investigate is between 

DVCs and deer population. Because actual deer population numbers are not available, a proxy must be used. 

We use buck-gun harvest per square mile (BHSQM). 

Given a panel data set of 88 counties from 1977 to 1998, 

we regress DVCs on BHSQM. As the results in column A 

of Table 1 illustrate, the coefficient on BHSQM is posiTable 1. Estimated coefficients on factors influencing deer-vehicle collisions in Ohioa

 

Dependent Variable: Deer Vehicle Collisions 

A B C D E Athens Williams

INTERCEPT (CONSTANT) 59.32*** -90.62*** -92.55*** -111.35*** -91.73*** -68.21 12.02

(0.00) (0.00) (0.00) (0.00) (0.00) (0.385) (0.841)

VEHSQM 0.80*** 0.80*** 0.78*** 0.75*** 0.79 0.827

(0.00) (0.00) (0.00) (0.00) (0.614) (0.373)

ODPSADJ 71.25*** 70.68*** 59.36*** 63.52*** 6.67 49.83***

(0.00) (0.00) (0.00) (0.00) (0.760) (0.006)

BHSQM 145.15*** 60.96*** 70.21*** 78.02*** 

(0.00) (0.00) (0.00) (0.00) 

DHSQM -5.72 -16.28*** 

(0.208) (0.001) 

BAG 24.55*** 

(0.00) 

BHSQMLAG 21.72* 

(0.055) 

DHSQMLAG -21.89*** 

(0.00) 

BAGLAG 24.12*** 35.83* -24.91

(0.00) (0.082) (0.152)

TOTAL 0.04*** 

(0.00) 

LIVEBUCKS 0.12*** 0.22***

(0.001) (0.00)

sample size (n) 1936 1936 1936 1936 1936 21 21

coefficient of 

 determination R2 0.48 0.74 0.74 0.75 0.75 0.95 0.92

a

 Panel data set from 1977-1998 across 88 counties. Estimated coefficients are estimated using Ordinary Least Squares and 

represent the slope of the linear regression line. Hausman test suggests a fixed-effects model. P-values are in parentheses. 

VEHSQM ~ number of registered vehicles in county per square mile; ODPSADJ ~ 0 for years 1977 to 1989 and 1 for years 

1990 to 1998; BHSQM ~ buck-gun harvest in county per square mile; DHSQM ~ doe-gun harvest in county per square mile; 

BAG ~ bag limit in county; BHSQMLAG ~ buck-gun harvest in county per square mile from previous year; DHSQMLAG ~ 

does-gun harvest in county per square mile from previous year; BAGLAG ~ bag limit in county from previous year; TOTAL 

~ total reported harvest of does and bucks in county for current year; LIVEBUCKS ~ estimated buck population in county. 

*,**,*** - statistically significant at the 10%, 5%, and 1% level, respectively.
95

tive and statistically significant at the 1% level. The R2

suggests that BHSQM alone seems to explain 48% of the 

variation in DVCs. 

In addition to the size of the deer population at a 

particular location, we hypothesize that traffic volume 

is an influential variable in predicting DVCs. As a proxy 

for traffic volume in each county, the number of vehicle 

registrations per square mile (VEHSQM) is used. After 

1989, though, there was a change in the accounting 

procedures used by the ODPS in calculating registered 

vehicles per county. To control for this change, we 

include a dummy variable (ODPSADJ) that is equal to 0 

for the years 1977 to 1989 and is equal to 1 for the years 

1990 to 1998. Essentially, the trend (slope) across these 

2 time periods is the same, but there is a level-effect 

induced by the alternative accounting procedure. By 

including this dummy variable, we capture and control 

for this level effect. The results from the regression 

of DVCs on both BHSQM and VEHSQM are shown in 

column B of Table 1. Both BHSQM and VEHSQM are 

of the expected sign and statistically significant at the 

1% level. Note that the coefficient on BHSQM is less 

than in the first regression, suggesting that some of the 

variation in DVCs attributed to BHSQM in column A was 

via correlation with VEHSQM and not necessarily direct 

causation. 

Clearly, does are involved in DVCs as well. To 

account for the impact of does on DVCs we include 

a proxy for the doe population, doe-gun harvest per 

square mile (DHSQM). As shown in column C, including 

DHSQM along with BHSQM and VEHSQM only mildly 

affects the coefficients on BHSQM and VEHSQM as compared to the previous regression. The coefficient on 

does is small, negative, and not statistically significant at 

even the 10% level. The lack of significance on DHSQM 

coupled with a drop in the t-statistics on the other coefficients (which are still statistically significant) suggest 

the presence of multicollinearity between DHSQM and 

BHSQM. Furthermore, while we would expect that the 

marginal impact of a buck on incidences of DVCs might 

be greater than the marginal impact of a doe, the negative sign on DHSQM deserves further explanation and 

investigation. 

ODNR’s management scheme has been quite 

consistent with respect to allowable buck harvest, 

essentially permitting 1 per year per hunter. Yet, ODNR 

has been increasingly aggressive in changing allowable 

doe harvest for population control purposes. It is well 

established that effective schemes for controlling deer 

populations rests with controlling the female population size. Thus observed changes in doe harvest rates 

are likely capturing changes in ODNR’s management 

scheme to control deer population size while observed 

changes in buck harvest rates, with virtually no management changes, are likely capturing actual changes deer 

population size. 

To illustrate how changes in allowable harvest 

impact DVCs, column D introduces the variable BAG, 

which is the total allowable harvest per hunter and 

varies across counties and over time. Since 1977, BAG 

has varied from a minimum of 0 to a maximum of 3 

total deer and at most a 1-buck harvest. Hence, changes 

in BAG essentially reflect changes in allowable doe harvest. As column D shows, accounting for the impact of 

management changes on DVCs leads to a statistically significant negative DHSQM coefficient. So while BHSQM 

is positively correlated with DVCs, and serves as a proxy 

for current population sizes, DHSQM is negatively correlated with DVCs and serves as a proxy for changes in 

future population sizes. The positive and statistically 

significant coefficient on BAG is expected given that it, 

along with DVCs and deer populations, has increased 

over time.

There are 2 issues that arise with using BHSQM 

and DHSQM as proxies for current and future deer 

populations to explain DVCs, both of which are related 

to the fact that most DVCs within a year occur before 

hunting season. First, changes in deer populations from 

changes in doe harvest are likely to be realized in 

the year following the harvest change. Considering that 

Ohio deer (gun) hunting season begins the Monday following the last Thursday in November, the impact on 

DVCs for any particular year from changes in harvest 

rates in that year are likely to be confined to only 

those DVCs that occur in December. This suggests that 

the previous year’s DHSQM may be a better proxy for 

population changes than current year DHSQM. Second, 

given that a buck seems to have a greater impact on 

DVCs than a doe, the previous year’s buck-gun harvest 

rate may be a good predictor of future DVCs. It should 

be emphasized, though, that such conclusions of the 

relative impact of does versus bucks on future DVCs are 

likely to depend on the sex ratio of the population. For 

instance, Feldhammer et al. (1986), while also emphasizing the importance of the sex ratio of the population 

on doe to buck DVC rates, report a 2:1 ratio of doe to 

buck DVCs.

Finally, while these 2 terms capture the impact of 

the previous year’s harvest on current year populations 

and DVCs, it important to also account for current year 

population effects. Since the correlation between any 

2 years BHSQM is high, we use the current year total 

harvest of does and bucks, TOTAL, as a current year 

population proxy. In doing so, we account for harvest 

effects, population effects, and management effects. 

In column E, we combine the previous year’s 

buck and doe harvest per square mile (BHSQMLAG and 

DHSQMLAG, respectively) with the current year’s total 

harvest (TOTAL). To be consistent with the impact of 

changes in management scheme on future populations, 

we also use the previous year’s bag limits – BAGLAG 

– rather than the current year’s bag limits. As the results 

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96

HUMAN CONFLICTS WITH WILDLIFE: ECONOMIC CONSIDERATIONS

suggest, this year’s population proxy – TOTAL – is 

positive and statistically significant as is the previous 

year’s buck-gun harvest rate, BHSQMLAG. The previous 

year’s does harvest rate, DHSQMLAG, is negative and 

statistically significant. All the other variables are of the 

expected sign and are statistically significant at the 1% 

level. 

The positive coefficient on BAGLAG can be interpreted as a signal that the ODNR recognizes populations are increasing. Given a particular bag limit, then, 

we would expect the increase in doe harvests will negatively impact DVCs. This sign may also be capturing the 

fact that a doe harvested may be a pregnant doe and 

thus, in effect, has a greater impact the future DVCs. 

The positive coefficient on the previous year’s buck 

harvest is still capturing increases in population as is 

the positive coefficient on TOTAL. 

Admittedly, there may be some noise involved 

in this analysis given the unit of aggregation is at the 

county-level, we employ numerous proxies, and we do 

not account for the use of mitigation strategies over 

time and across counties. In an effort to investigate 

the relationships suggested in equation (1) in a more 

disaggregate manner, we use data collected by the Ohio 

Department of Transportation (ODOT) on DVCs along 

3 1-mile road segments in rural Ohio (C. Schreck, 

Ohio Department of Transportation, unpublished data). 

Table 2 provides a summary of these data. The data 

were collected from 1990 to 1998 on DVCs along a 

single-mile road segment of SR 15 in Defiance County, 

Ohio, and 2 separate single-mile road segments along 

US 68 and US 224, in Hancock County. Both of these 

counties are in northeastern Ohio.

The purpose of the ODOT in collecting these 

data was specifically to investigate the effectiveness of 

Swareflex reflectors. Along SR 15 in Defiance County, 

the Swareflex reflector was installed in May of 1995. 

The single accident reported along SR 15 in 1995 

occurred after the installation of the reflector. Along 

US 68 in Hancock County, the Swareflex reflector was 

installed in February of 1995. Both reported DVCs 

along US 68 in 1995 occurred after the installation of 

the reflectors. The Swareflex reflectors were installed 

along US 224 in November of 1993. Of the 2 accidents 

that occurred along US 224 in 1993, 1 accident occurred 

before installation of the reflector while the other accident occurred after the installation. Finally, while there 

seems to be continuing efforts by the ODOT to collect 

similar data for future analyses, at the time of this analysis, we were limited to analyzing the data presented in 

Table 2. Table 2 lists both the total number of DVCs per 

year along each segment and also a subset consisting of 

those DVCs that occurred at night alone. 

Given the small sample size associated with each 

site, the 9 observations from each site are pooled for a 

total of 27 observations. Grass was discovered covering 

1 of the reflectors along US 68 in Hancock in 1996, 

and thus we dropped this observation from the data set 

leaving us with 26 observations. Because the dependent 

variable is a non-negative integer whose mean is close to 

0, a more appropriate modeling strategy is to use some 

count-based estimator. We employ a Poisson model. A 

likelihood ratio test suggested that we reject the null 

hypothesis of a pooled Poisson estimator in favor of a 

panel Poisson estimator. A Hausman test suggested that 

the appropriate econometric approach was a randomeffects model. Since neither traffic volume data nor 

deer density data were available for any of the individual sites, county-level data (TOTAL, VEHSQM, and 

BHSQMLAG) were used as proxies to capture potential 

county-level trends associated with these factors. While 

we used both BHSQMLAG and DHSQMLAG with the 

county-level aggregate data in Table 1, we use only 

BHSQMLAG here. Given the low degrees of freedom 

and the result that BHSQMLAG seems to have a greater 

impact on DVCs than DHSQMLAG, choosing BHSQMLAG seemed appropriate. Finally, to account for the 

effect of the reflectors on DVCs, we use a dummy variable (REFLECTO) that is equal to 0 for the years (and 

DVC incidences) before the installation of the reflectors 

and equal to 1 for the years (and DVC incidences) after 

the reflector installation.

To get an understanding of how the relationships 

derived from combining more micro-level data from 

Table 2. Annual Deer-Vehicle Collisions Along 3 Highway Segments in Ohioa 

Annual Number of Deer-Vehicle Collisions 

1990 1991 1992 1993 1994 1995 1996 1997 1998

Defiance Night 3 5 2 8 0 1 1 2 2

 SR 15 Total 4 6 2 8 0 1 3 2 2

Hancock Night 2 5 3 11 10 1 7 5 2

 US 68 Total 3 6 4 14 14 2 8 5 3

Hancock Night 0 3 0 2 3 0 0 1 0

 US 224 Total 2 4 0 2 3 0 1 2 2

a

 Each highway segment is approximately 1 mile long.

Swareflex reflector installed on SR 15: 5/95. Accident in 1995 occurred after reflector installed. 

Swareflex reflector installed on US 68: 2/95. Both accidents in 1995 occurred after reflector installed.

Swareflex reflector installed on US 224: 11/93. In 1993, 1 accident occurred after and 1 before reflector installation.

1996 observation for US 68 dropped due to poor maintenance of reflector
97

these 3 highway segments with aggregate proxies compare to the aggregate results from Table 1, columns A 

and B of Table 3 show the results from regressing DVCs 

– both total and nighttime total – on registered vehicles 

per square mile (VEHSQM), a 1-year lag of the buck-gun 

harvest per square mile (BHSQMLAG), and total harvest 

of does and bucks (TOTAL). While all the coefficients 

are of the expected signs and similar to the analysis provided in Table 1, VEHSQM is not statistically significant 

in either column A or B. This result is not surprising 

given the potential noise associated with using vehicle 

registration at the county-level to capture traffic densities along these 3-mile segments. 

In columns C and D the impact of Swareflex 

reflectors on the incidences of DVCs is analyzed. Obviously, these reflectors do not work during the day. Yet, 

it is interesting to note that whether our dependent variable measures total DVCs or only those DVCs occurring 

at night, the coefficient on REFLECTO is negative and, 

surprisingly, statistically significant when the dependent 

variable is total DVCs. 

While the sign on these results may suggest 

promise for the use of reflectors on DVC reduction, 

there may be some correlation between the installation 

of the reflectors and, perhaps, some type of aggressive 

population management scheme. To account for potential deer management effects, in column E we include 

the previous year’s bag limit. As illustrated, the sign on 

REFLECTO is still negative, yet as with column D, it is 

not significant at the 10% level. 

Obviously the small sample size and fewer 

degrees of freedom are of concern. These results suggest that the robustness of the impact of reflectors on 

DVCs, while consistently negative, varies in statistically 

significance. Furthermore, while these results may be 

construed to suggest a potential negative impact on 

DVCs from installing reflectors, before any conclusions 

are drawn the results should be compared with theory. 

That is, from a biological perspective, why might deer 

behave differently in the presence of the reflectors? 

While this issue does not seem to be resolved completely, work by Ujvari et al. (1998) suggested that deer 

do not behave differently, at least after habituation, 

to the presence of the reflectors. Perhaps drivers are 

responding differently in the presence of the reflectors 

(Zacks 1986). 

While we acknowledge that there are difficulties 

with both sets of models presented above, particularly 

the level of aggregation of the data in Table 1 and the 

small sample size and lack of road specific information on traffic volume and deer populations in Table 

3, these results coincide with some findings in the 

literature. First, both traffic volume and deer populations would seem to impact DVCs positively (Tonkovich 

1995, Romin and Bissonette 1996b). Second, reducing 

the deer population, particularly the buck population, 

Table 3. Estimated coefficients on factors influencing deer-vehicle collisions along three highway segments in Ohioa

Dependent Variable: Deer-Vehicle Collisions 

A B C D E

Total DVCs Night DVCs Total DVCs Night DVCs Night DVCs 

INTERCEPT (CONSTANT) 2.25 2.30 -0.36 -0.42 -0.13 

(0.151) (0.214) (0.849) (0.848) (0.947)

VEHSQM -0.006 -0.009 0.014 0.006 0.012 

(0.536) (0.454) (0.271) (0.699) (0.334)

REFLECTO -0.78** -0.59 -0.58

(0.02) (0.137) (0.114)

TOTAL 0.003*** 0.003*** 0.002*** 0.003*** 0.002*** 

(0.000) (0.000) (0.009) (0.002) (0.002) 

BHSQMLAG -3.66*** -4.39*** -2.05 -3.21** -2.54*

BAGLAG -0.15

(0.18) 

sample size (n) 26 26 26 26 26 

18.28 20.96 21.82 21.52 24.09

 (0.0004) (0.0001) (0.0002) (0.0002) (0.0002) 

a

 Panel data set from 1990-1998 over 3 segments. Locations include a 1-mile highway segment in Defiance County and 

2 distinct 1-mile segments in Hancock County. Given the non-negative integer values for the dependent value, the 

coefficients are estimated using a Poisson model. P-values are in parentheses. Hausman test suggests a random effects 

model. Wald test statistic for nonlinear models tests joint hypothesis that all coefficients are zero. A likelihood ratio test 

rejects the null hypothesis of a pooled estimator. VEHSQM ~ number of registered vehicles in county per square mile; 

REFLECTO ~ zero/one dummy variable for presence of reflector; TOTAL ~ total reported harvest of does and bucks in 

current year; BHSQMLAG ~ buck-gun harvest in county per square mile from previous year; BAGLAG ~ bag limit in county 

from previous year; *,**,*** - statistically significant at 10%, 5%, and 1% level, respectively.

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HUMAN CONFLICTS WITH WILDLIFE: ECONOMIC CONSIDERATIONS

may be an effective means of reducing DVCs. Indeed, 

research by Romin and Bissonette (1996b) suggested 

that bucks accounted for proportionately more DVCs 

than their representation in the population. Third, 

Swareflex reflectors are negatively correlated with DVCs 

along 3 road segments in rural Ohio. Finally, given that 

the pooled estimators are consistently rejected in favor 

of panel estimators, locational factors or habitat characteristics that differ across counties seem to play a 

significant role in explaining DVCs.

While these results may or may not be surprising, 

they do emphasize both a reasonable approach for 

investigating these issues and the importance of more 

detailed analyses investigating even further the factors 

giving rise to DVCs. For example, the work by 

Bashore et al. (1985) and Romin and Bissonette (1996b) 

accounted for specific characteristics of habitat and 

roadway construction in the investigations of DVCs. 

Such detailed analyses provided the types of information necessary for policy makers to make informed decisions. And while our analysis is somewhat more general, 

the results suggest that such detail is needed to fully 

understand the factors influencing DVCs. 

Noticeably absent from this discussion of the 

characteristics of DVCs in Ohio has been any mention of 

the costs. Obviously, the costs per accident are going to 

depend on a number of factors, including size of deer, 

type of vehicle, speed of vehicle, and insurance rates. 

While DVC economic-related data is limited, the ODPS 

does keep statistics on the number of DVCs. ODPS has 

DVC crash-related statistics on both the seriousness of 

the crash and a dollar value attached to each crash 

based on the National Highway Traffic Safety Administration’s (NHTSA) 1991 estimates of the average cost 

per accident, categorized according to the reported 

seriousness of the accident (personal communication, 

D. Bowens, Statistical Supervisor, ODOT, 1998). Using 

ODPS data from 1990-1998, there were a total of 143,016 

reported DVCs. Recall that for a deer-related vehicle 

accident to be reported as a DVC requires a minimum 

of US$150 of damages. Of the 143,016 reported accidents, 14 resulted in human fatalities resulting in an 

approximate 0.01% probability of death. Of those accidents having some type of morbidity effects, 247 (0.17%) 

resulted in serious injuries, 3,844 (2.7%) resulted in mild 

injuries, and 6,892 (4.82%) resulted in claimed injuries. 

Finally, as mentioned above, there were 132,019 (92.3%) 

accidents that were reported but did not result in any 

claimed injuries. 

The ODPS reports the average cost per vehicle 

accident to estimate annual losses from DVCs. These 

average costs are categorized by the reported seriousness of the accident (death, serious injury, mild injury, 

claimed injury). The expected cost of a DVC is calculated by summing across each “seriousness” category 

the average cost associated with the seriousness of the 

event multiplied by the probability of that particular 

event. Following this approach, the expected cost associated with the death component of a DVC is US$235, 

US$289 for a serious injury, US$891 for a mild injury, 

and US$819 for a claimed injury. These estimates are 

based on the following loss or cost per event (ODPS 

1998): US$2,393,000 (death), US$170,000 (serious 

injury), US$33,000 (minor injury), and US$17,000 

(claimed injury). Finally, since at least US$150 of damages must be incurred for it to be reported (therefore 

imposing a lower bound on reported collisions), US$138 

is the expected cost for a collision without any type 

of injury. Clearly, this is an underestimate of the costs 

strictly associated with automobile damage and should 

be updated when more accurate information becomes 

available. Summing up these expected costs gives us an 

average DVC cost estimate of approximately US$2,372. 

While these cost estimates do not come from actual 

DVC accidents, it is not evident that a reported serious 

or mild injury from a DVC should differ greatly from a 

reported serious or mild injury from some other type of 

vehicle accident. 

The next section will combine this information 

on costs with a few simple estimated relationships 

between DVCs and harvest rates to evaluate the impacts 

of DVCs on optimal deer management, especially with 

respect to the desired optimal steady state population 

size.

SIMULATING CHANGES IN DVCS WITH A 

DYNAMIC POPULATION MODEL

Methods

From a practical perspective, there are 2 general 

means for reducing the incidence of DVCs in a given 

area: through the active use of DVC mitigation practices or by reducing the population of deer through 

changes in hunting regulations. Both DVCs and harvest 

depend on the dynamics of deer population growth 

and this growth in turn depends on DVCs and harvest 

(e.g., McCullough 1979, Downing and Guynn 1983, 

Guynn 1985). To facilitate an analysis of changes in the 

incidence of DVCs on harvest rates and deer population growth, we represent the dynamic interactions 

between harvest, DVCs, and the population of whitetailed deer in Ohio with a simple system of ordinary 

differential equations (ODEs) that can be numerically 

solved. The 3 principle populations of concern are 

bucks (adult males), does (adult females), and fawns 

(juvenile deer). We can simulate the effects of changes 

in DVCs and hunting regulations on the size and growth 

of these populations by allowing the behavior of the 

ODE system to be controlled through parameter restrictions. Consequences of these changes can then be evaluated by comparing the characteristics of the system’s 

solution before and after the regulatory change. 
99

Growth of an exploited population over time 

will be a function of the natural growth rate and the 

rate at which the population is harvested and otherwise killed. For this analysis, we overlook the impact of 

immigration and emigration on the natural growth rate 

of the population, or alternatively, we assume these 2 

effects net to 0 in a steady-state scenario. Given that the 

natural growth of the deer population is also subject 

to constraints imposed by the characteristics of the 

environment, we assume that the natural growth of the 

population follows a logistic pattern subject to annual 

harvest and DVC mortality. Logistic growth is a modification of standard exponential growth, and is commonly employed in modeling natural populations. The 

characteristic ‘S’ shape of the logistic function reflects 

the fact that due to crowding and limitations on natural 

resources, exponential growth cannot continue indefinitely. At the beginning of the logistic curve, where 

population size is relatively small and carrying capacity 

constraint not binding, growth approximates exponential growth. As population increases and the carrying capacity is approached, growth begins to saturate 

(Luenberger 1979). Logistic natural growth in a particular area c, then, can be described by the first term in 

each of the following differential equations: 

dBc/dt = bBBc(1 – Bc/kBc) – hBBc – vBBc (2) 

dDc/dt = bDDc(1 – Dc/kDc) – hDDc – vDDc (3)

where B and D represent the sizes of the buck and 

doe populations respectively, bB and bD are the intrinsic 

natural growth rate of the buck and doe populations, 

respectively, and k is the carrying capacity of the environment. From this logistic growth we subtract hunting 

mortality (at rate h times the population size) and vehicle mortality (at rate v). We also model the unharvested 

fawn population as a simple function of the equilibrium 

doe population, based on the number of fawns each doe 

produces annually: 

Fc = 1.7Dc (4)

Fawns are harvested at some rate, hf, and killed 

by collisions with vehicles at rate vB. Given a set of 

values for the 4 parameters of each differential equation and for the fawn harvest and DVC rates, we can 

solve for the population sizes necessary for the system 

to be in a steady state by setting each of the ordinary 

differential equations equal to 0. As the deer population in Ohio has been approaching a stable level due 

to aggressive management policies, rather than solving 

for equilibrium population size we instead impose the 

assumption that the populations are in equilibrium. 

Given this assumption, we can solve for the growth rate 

that allows the current stable population to equal our 

estimate of the present deer population. In essence, we 

use the intrinsic natural growth rate as the calibration 

parameter.

Results

To use our model to simulate the effects of 

changes in the incidences of DVCs on deer population 

size and harvest, we must first calibrate out model such 

that the equilibrium population size matches the estimated size of the current population of bucks in Athens 

County. In Athens County we have an estimate of the 

1996 (assumed to be stable) population size of approximately 2,300 bucks using a buck-harvest rate of 63% 

-- i.e., hB is equal to 0.63. Employing suggested ratios of 

bucks to does and does to fawns, the number of deer 

that can be supported per square mile, and the size of 

Athens County, we derive a carrying capacity for bucks 

of 4,658. This estimate is based on conversations with 

ODNR Division of Wildlife officials (1999). Specifically, 

we assume a 1.6:1 ratio of does to bucks and a springtime 1.7:1 fawn to doe ratio. Finally, we assume 1996 

populations are being managed at 55% of maximum 

carrying capacity. 

To estimate the relationship between bucks and 

DVCs, vB, we transform our buck-harvest per square 

mile data in Athens County to a buck population estimate (LIVEBUCKS) and regress DVCs on this factor and 

VEHSQM. The buck population, LIVEBUCKS, is calculated by dividing BHSQM for Athens County by 0.63 and 

multiplying that estimate by the area of the county. As 

Table 1 illustrates under the Athens column, a 1-unit 

increase in the number of bucks in Athens County, controlling for vehicles per square, leads in an increase in 

DVCs of 0.12. Given the small sample size and concerns 

about our degrees of freedom, we limit our explanatory 

variables to these 2 factors – both of which have been 

shown to be quite robust to other specifications.

Given a calibrated model, we can now simulate 

the effects of changes in the incidence of DVCs on 

deer population size and harvest. For example, assuming that the initial DVC rate is 0.12 for bucks and 0.04 for 

fawns and does, we can simulate the effects of decreasing this rate through mitigation practices by altering its 

value and solving for new equilibrium conditions. Alternatively, we can assume that the DVC rate remains constant, but that the number of DVCs is reduced through 

changes in harvest pressure (currently 0.63 for bucks 

and 0.20 for fawns and does). Finally, we can examine a 

combination of these means to reduce DVCs. 

We assume that the installation of a particular 

DVC mitigation strategy will reduce DVCs by 85%. 

While we chose 85% for illustrative purposes and 

emphasize that other estimates would have illustrated 

our intentions, conversations with wildlife mitigation 

strategy experts (Romin 1996, personal conversation, 

Neve, D.G., Hi-tech fences 1996, personal conversation) 

suggest Z-clip fencing can achieve an 85% effectiveness reduction. At current harvest rates, then, an 85% 

decrease in the DVC rate in Athens County causes the 

equilibrium population of deer to increase such that 

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100

HUMAN CONFLICTS WITH WILDLIFE: ECONOMIC CONSIDERATIONS

185 additional bucks, 153 additional does, and 230 additional fawns could be harvested per year. Coupled with 

the change in population size, this 85 % decrease in 

DVCs translates into approximately 550 fewer accidents 

with deer each year in Athens County. Conservatively 

estimating the value of a harvested deer at US$180 

(Schwabe et al. 2001) and using the cost per DVC estimate of US$2,372 described above, an 85% decrease in 

the DVC rate results in approximately US$102,240 in 

additional benefits to hunters and US$1,304,809 in DVC 

cost savings. Based on these results, we can conclude 

that strategies to reduce DVCs in Athens County that 

can be implemented at a cost of US$1,407,049 or less 

per 85% reduction in DVCs are economically efficient. 

It is important to note that changes in deer populations are likely to lead to changes in the size of the 

deer harvested in a similar manner as suggested by 

Guynn (1982). Such changes in the size of the deer may 

have impacts on the value of a deer. Also, while we do 

not differentiate here between the value of a buck, doe, 

or fawn, acknowledging such differences may also have 

potentially large impacts on the value of the harvest and 

thus the over-all benefits estimate.

It is also important to acknowledge that because 

deer populations differ markedly across the state, the 

effects of a given percentage reduction in DVCs will 

vary across counties. For example, we can contrast the 

above results for Athens County (located in the eastern 

part of Ohio) with those for Williams County (located 

in the western part of Ohio). As highlighted in Table 

1 under the Williams column, Williams County has a 

DVC to buck ratio of 0.22. Given this coefficient, a 

different carrying capacity, and the same assumptions 

regarding doe and fawn harvest and DVC rates as in 

Athens County, an 85% reduction in the DVC rate will 

result in the opportunity to harvest 65 additional bucks, 

48 additional does, and 72 additional fawns. Furthermore, there will be approximately 196 fewer accidents. 

Such results, using the same dollar values as above, lead 

to approximately US$33,300 in benefits to hunters and 

US$464,986 in cost savings from accidents.

An alternative to installing DVC mitigation strategies is to alter the hunting regulations so that greater 

hunting pressure will decrease the size of the deer 

population and in theory, lead to fewer DVCs. This 

could be accomplished by either increasing the length 

of deer hunting season or issuing more deer hunting 

permits. For illustrative purposes, we assume that a 

7.6% increase in the deer harvest rate could be achieved 

by increasing the length of Ohio deer hunting season 

by 1 day – in effect changing current deer (gun) season 

in Ohio from 13 to 14 days. In Athens County, such a 

change would decrease the equilibrium populations 

of bucks, does, and fawns such that approximately 50 

fewer deer would be harvested (14 does, 21 fawns, and 

15 bucks) and 55 fewer DVCs would occur. Because 

of density dependent responses in productivity and 

recruitment, a decline in harvest would be unlikely 

in most situations today. However, productivity data 

from Ohio strongly suggests that herds in the eastern 

and western portions of the state reside at maximum 

sustained yield (MSY) and very likely to the left of MSY, 

respectively. In economic terms, 50 fewer deer and 55 

fewer DVCs lead to US$9,000 in lost benefits to hunters 

but US$130,460 in reduced costs from DVCs.

In Williams County, our estimates suggest that 

total deer harvested would stay approximately the same, 

although there would be 18 fewer DVCs, leading to a 

cost savings of US$42,696. The reasoning behind this 

latter outcome is that while harvest totals remain the 

same, the composition of harvest changes. In Williams 

County, increasing harvest rates for each population 

results in harvesting 5 additional bucks, 2 fewer does, 

and 3 fewer fawn as compared to the pre-harvest 

increase. The desirability of such a policy change will 

likely depend on the relative sizes of the losses in benefits to hunters and the potential gains in terms of net 

cost savings from DVCs. 

Finally, we simulate the effects of a combination 

policy where both hunting regulations and active DVC 

mitigation practices are employed. We use the same 

values from the individual simulations above, an 85% 

reduction in the likelihood of a DVC and a 7.6% increase 

in harvest rate. In Athens County, this combination of 

policies causes a net increase in the size of all deer populations, such that total deer harvested can increase by 

approximately 580, and 560 fewer DVCs will occur. This 

combination of benefit increases and cost reduction 

leads to a gain of US$1.4 million. In Williams County, 

total harvest will increase by approximately 200 deer – 

75 bucks, 50 doe, and 75 fawn – and DVCs will decrease 

by an equal amount for a total gain of over US$500,000. 

Clearly this combination policy results in higher net 

benefits than either policy alone. But again, cost estimates need to be considered to satisfy efficiency conditions. 

While the results of these simulations depend 

critically on the assumed growth functions, the accuracy of our calibrations, and the assumed harvest and 

mortality rates for buck, does, and fawns, we can form 

some general conclusions. First, mitigation strategies 

that are effective have the opportunity to provide 

substantial benefits to both drivers and hunters alike. 

Before a conclusion can be drawn about the efficiency 

of such strategies, though, the benefits of implementing 

any mitigation strategy must be compared to the costs 

of implementation. Second, changing hunting regulations are another means of reducing DVCs with added 

potential benefit to hunters from greater harvests. An 

implicit assumption of course is that herds are currently being managed as most are, somewhere between 
101

65% and 80% of carrying capacity. While aggressive 

antlerless harvests will reduce overwinter herd size 

and presumably DVCs, harvests should increase due 

to density dependent responses in productivity and 

recruitment. Herds managed according to social tolerances rather than biological limits, such as those in 

western Ohio and other heavily farmed sections of the 

Midwest, would likely experience not only a reduction 

in DVCs with a population reduction, but also a decline 

in harvest as well. 

While the benefits of changing population size 

might seem modest when compared to implementing 

some type of DVC mitigation strategy, the costs of implementation are likely to be more modest as well. Furthermore, changing population size does not include 

dealing with the uncertainty surrounding the potential 

effectiveness of many DVC mitigation strategies. Finally, 

the combination of implementing a DVC mitigation strategy and changing population size via hunting regulations is shown to offer the largest potential benefits. 

Yet again, judgments as to the efficiency of this strategy 

relative to the other strategies must be deferred until 

implementation and maintenance costs are introduced. 

Regardless of strategy though, as long as the potential 

gains from reducing DVCs via changes in hunting regulations or mitigation strategies are greater than the costs 

of implementing these strategies, a potential economically efficient improvement is available.

CONCLUSION

More than 500,000 DVCs occur annually in the 

US and resulting in losses in excess of US$1.2 billion 

from property damage, human injury, and human 

mortality. In Ohio, with roughly 26,000 DVCs per year, 

the estimated annual losses are greater than 3 times the 

revenue generated from the sale of hunting licenses and 

deer permits combined. This research has investigated 

factors that seem to influence the incidences of DVCs 

in Ohio. Similar to the conclusions found in research 

by Allen and McCullough (1974), Culbertson and Stoll 

(1990), and Romin and Bissonette (1996a), our results 

suggest that factors such as population sizes, traffic 

volume, habitat, and time all influence the incidences of 

DVCs in Ohio. Using aggregate county-level data, trends 

in vehicle registration and buck-gun harvest per square 

mile are strongly correlated with DVCs and, based on 

the specifications evaluated in Table 1, are quite robust. 

Furthermore, after accounting for changes in bag limits 

across counties and over time, doe-gun harvests per 

square mile also prove to be strongly correlated with 

DVCs. Given that the ODNR manages populations by 

changes in allowable doe harvest while leaving buck 

harvest management schemes relatively unaltered, our 

results are not surprising. Yet, given that optimal deer 

management may require adjustments to population 

size when confronted with such exorbitant humandeer conflict, additional information that the marginal 

impact of a buck on DVCs is greater than the marginal 

impact of a doe may prove useful. 

Such results are not simply fodder for academic 

grist. For instance, our results suggest that while reducing populations will likely lead to fewer DVCs, targeting 

the buck population will have a larger effect than targeting doe populations. Furthermore, given the very 

predictable geographic pattern of white-tailed deer in 

Ohio, the strong correlation between proxies for traffic 

volume and incidences of DVCs, and the large potential 

benefits from reducing the rate at which DVCs occur, 

public awareness campaigns educating drivers of these 

characteristics would seem to be both a potentially 

effective and rewarding mitigation strategy. Indeed, 

given the apparent lack of research investigating the 

impact of this latter mitigation strategy, our results suggest that the potential gains from more research could 

be large. 

Finally, we considered 2 general strategies for 

reducing DVCs – reducing the rate at which DVCs occur 

via mitigation strategies and using hunting regulations 

to reduce the deer population. While both methods 

show potential gains, a combination of the 2 seemed 

to be the most effective and may provide the largest 

net gains. Clearly, the results will depend on the costs 

of implementing these strategies. Furthermore, given 

uncertainty about the effectiveness of DVC mitigation 

strategies, the relationship between reductions in deer 

populations and DVCs, and the relationship between 

deer saved from DVCs and deer harvest, our results 

should be read with caution. That is, it is clear that the 

assumptions used in this analysis are at least in part 

driving the results. However, it is important to note that 

while relatively simple, the models used in this work 

do show a great deal of promise in providing explicit 

empirical linkages between hunting policy, deer vehicle 

collisions and economic values. 

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DEER-VEHICLE COLLISIONS